Author: Kai Sonder

Global map of wheat mega-environments

Kai Sonder (2016)

Global map of wheat mega-environments. The map show twelve different mega-environments around the world.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Global map of maize mega-environments

Kai Sonder (2016)

Global map and shape files of maize mega-environments. The map show six different mega-environments around the world.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

FAO-SIAC Estimating CA adoption in Sinaloa, Mexico (calibration sites)

Kai Sonder Guillaume Chomé (2017)

Use of remote sensing based radar images for zero tillage detection in Sinaloa, Mexico.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

FAO-SIAC Estimating CA adoption in Sonora, Mexico (calibration sites)

Kai Sonder Guillaume Chomé (2017)

Use of remote sensing based radar images for zero tillage detection in Sonora, Mexico.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

FAO-SIAC Estimating CA adoption in Guanajuato, Mexico (calibration sites)

Kai Sonder Guillaume Chomé (2017)

Use of remote sensing based radar images for zero tillage detection in Guanajuato, Mexico.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

FAO-SIAC Estimating CA adoption in Sinaloa, Mexico (calibration sites)

Kai Sonder Guillaume Chomé (2017)

Use of remote sensing based radar images for zero tillage detection in Sinaloa, Mexico.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

FAO-SIAC Estimating CA adoption in Sinaloa (Municipality of Santiago, El Fuerte and Guasave), Mexico (ground-truthing sites)

Kai Sonder Guillaume Chomé (2017)

Use of remote sensing based radar images for zero tillage detection in Sinaloa (Municipality of Santiago, El Fuerte and Guasave), Mexico.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

Evaluating Radar Satellite Based Remote Sensing for Conservation Agriculture Adoption Detection in North and Central Mexico

Kai Sonder Guillaume Chomé (2018)

Conservation agriculture has been tested and out scaled for over 60 years in rural areas of Mexico, however the rate of adoption and dis adoption, as well as the current area under CA is unknown. Estimates range between 50,000 ha and over 800,000 ha depending on the source. Studies and surveys in several states where CA was propagated in the past show an unclear picture. Tillage and crop residue detection based on remote sensing data has been successfully tested since the mid-1980s and seems a valid technology to essay larger areas or countries with minimal cost based on freely available satellite image sources. A promising approach utilizing radar imagery from Sentinel 1A developed in Belgium for tillage recognition was chosen for the current study. Radar imagery having the advantage of not being affected by clouds or haze. In parallel work being done in the Indo Gangetic Plains as a collaboration between the Université catholique de Louvain and CIMMYT images for areas in the states of Sonora and Sinaloa in Northern Mexico as well as Guanajuato in Central Mexico were acquired from ESA and analyzed. Initial results for Sinaloa show an average accuracy of 94% for predicting tillage type. Current limitations of widespread utilization of the technology include the need for availability of spatial data delineating field boundaries in order to clearly identify cropped and non cropped areas as well as the association of crop management data such as irrigation timings and crop types. Some of this is expected to improve in the near future with big data and crowd sourcing applications for field boundary detection. The results from the associated study in India indicate however that there is good potential to utilize this also in areas with smaller field sizes and utilizing Sentinel 2 data for segmentation of landscapes to substitute detailed field boundary data.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA

SeeD GWAS Panel Accession Collection Site GIS-Derived Environmental Data

Sarah Hearne Kai Sonder (2018)

Historic accession collection site environmental data derived from CRU and other data resources.

Dataset

CIENCIAS AGROPECUARIAS Y BIOTECNOLOGÍA